Aim: We propose a simple tool for early prediction of unfavorable long-term evolution of multiple sclerosis (MS). Methods: A Bayesian model allowed us to calculate, within the first year of disease and for each patient, the Bayesian Risk Estimate for MS (BREMS) score that represents the risk of reaching secondary progression (SP).
Results: The median BREMS were higher in 158 patients who reached SP within 10 years in comparison with 1087 progression-free patients (0.69 vs. 0.30, p<0.0001). BREMS value was related to SP-risk in the whole cohort (p<0.0001) and in the subgroup of 535 patients who had never been treated with immune therapies, thus fairly representing the natural history of disease (p<0.000001).
Conclusions: BREMS can be useful both to identify the patients who are candidates or not for early or for more aggressive therapies, and to improve the design and the analysis of clinical therapeutic trials and of observational studies.
- clinical research methods
- disease progression
- multiple sclerosis